Introduction

Bowel preparation quality is a key determinant of colonoscopy effectiveness and adenoma detection^1,2. Manual evaluation of free-text colonoscopy reports is labor-intensive and limits quality monitoring³. Developing a natural language processing (NLP) application for measuring colonoscopy quality demonstrates the potential to automate this process by extracting key quality indicators⁴.

Aims & Methods

We aimed to develop and validate a NLP approach to automatically classify bowel cleanliness and to comprehensively structure colonoscopy reports by extracting procedural and pathological variables. A total of 11,374 reports from our institutional endoscopy unit were analyzed. An annotated dataset of 1,018 reports labeled as “good,” “intermediate,” or “poor” preparation was used to train a TF-IDF–based logistic regression classifier. Cross-validation and test performance were evaluated using accuracy, precision, recall, and F1-scores, and the model was applied to the full dataset. Additional variables (cecal intubation, polyp presence, number, size, localization, polypectomy method, and hemostasis application) were extracted using rule-based NLP heuristics. Adenoma detection rate (ADR) was calculated in a predefined subset (January 2023–July 2025) with complete pathology data, and multivariable logistic regression identified independent predictors.

Results

The NLP classifier achieved a cross-validated accuracy of 92%. Precision and recall were highest for “good” preparation (0.98 and 0.99) and acceptable for “intermediate” (0.85 and 0.82) and “poor” (0.85 and 0.87). Application to the full cohort yielded 5,787 (50.9%) good, 2,176 (19.1%) intermediate, and 3,411 (30.0%) poor preparations. Leveraging these structured data, ADR analysis in the subset of 5,420 patients showed an overall rate of 20.4%. Multivariable analysis demonstrated that ADR was independently associated with older age (OR 1.05 per year, 95% CI 1.05–1.06, p<0.001), male sex (OR 1.74, 95% CI 1.51–2.01, p<0.001), adequate bowel preparation (OR 1.46, 95% CI 1.21–1.75, p<0.001), cecal intubation (OR 1.76, 95% CI 1.43–2.16, p<0.001), and procedures performed by specialists compared with trainees (24.0% vs 20.3%; OR 1.25, 95% CI 1.05–1.47, p=0.010).

Conclusion

This large-scale real-world analysis demonstrates the feasibility of NLP-based automated classification of bowel preparation quality and its direct utility in generating structured colonoscopy datasets for quality monitoring. By integrating bowel cleanliness with other performance indicators (age, sex, cecal intubation, operator expertise), our framework not only achieved high classification accuracy but also provided clinically relevant benchmarks for ADR, supporting targeted quality improvement in endoscopy units.

References

1. Clark BT, Protiva P, Nagar A, et al. Quantification of Adequate Bowel Preparation for Screening or Surveillance Colonoscopy in Men. Gastroenterology. 2016;150(2):396-e15. doi:10.1053/j.gastro.2015.09.041
2. Clark BT, Rustagi T, Laine L. What level of bowel prep quality requires early repeat colonoscopy: systematic review and meta-analysis of the impact of preparation quality on adenoma detection rate. Am J Gastroenterol. 2014;109(11):1714-1724. doi:10.1038/ajg.2014.232
3. Raju GS, Lum PJ, Slack RS, et al. Natural language processing as an alternative to manual reporting of colonoscopy quality metrics. Gastrointest Endosc. 2015;82(3):512-519. doi:10.1016/j.gie.2015.01.049
4. Harkema H, Chapman WW, Saul M, Dellon ES, Schoen RE, Mehrotra A. Developing a natural language processing application for measuring the quality of colonoscopy procedures. J Am Med Inform Assoc. 2011;18 Suppl 1(Suppl 1):i150-i156. doi:10.1136/amiajnl-2011-000431